Raghav Mehra

Cryptanalysis-resistant SDN architecture for dynamic traffic management using applied algebra and elliptic curve cryptography for secure tenant isolation

Ashok Saini, Vikas Thada, Sanat Jain, Swati et autres

Software-Defined Networking (SDN) security is enhanced using algebraic structures and elliptic curve cryptography for secure, scalable, multi-tenant communication.The model ensures robust key exchange, authentication, and traffic isolation while resisting cryptanalytic attacks like replay and MITM, improving efficiency and resilience over traditional SDN …

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LLM-Powered Intelligent Document Analyzer for Educational Content Understanding

Phadtare Nutan Devram, Gadhagoni Sharath, Raghav Mehra

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AI-Enabled Multimodal Yoga Posture Assessment with Physiological Feedback

Muvvala Jethasri, Harpuneet Kaur, Shatakshi Bhardwaj, Raghav Mehra

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Optimizing Cloud Migration: A Case Study Approach to Application Modernization

Tapsi Nagpal, Shiv Kumar, Vijay Mohan Shrimal, Girish Paliwal et autres

One of the most basic but important problems faced by websites or applications is sudden increases in user traffic. The main ideology of this project is to reduce the traffic issues over these websites or applications. To reduce the workload over a …

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An Optimized & Data-Driven Approach for Real-Time Slot Allocation and User-Friendly Access in Smart Parking System

Tapsi Nagpal, Shiv Kumar, Vijay Mohan Shrimal, Girish Paliwal et autres

Since parking allocation being one of the main challenges in modern cities, the need for a smart parking system has emerged. This paper carries out an in-depth study, comparison, and analysis of various SPS that focus on the technological approaches, sensors, networking …

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Towards Next-Generation Sustainable Cancer Diagnosis: A Hybrid Transformer-Enhanced Texture-Attention Framework with Multi-Objective Optimization

Kanishk Agrawal, Javed Wasim, Raghav Mehra

Sustainable cancer diagnosis with histopathological images has not yet been achieved as high computational cost, class imbalance, and lack of model interpretability are present. The currently available CNN and transformer-based models are computationally expensive, resulting into high performance, but limit their clinical …

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Interpretability in Cardiovascular Diagnostics: Classical Machine Learning vs. Deep Learning

Saroj Kumari, Meena Chaudhary, Raghav Mehra

An overview of the integration of Al-based ECG applications in different cardiovascular disorders is given in this study. Globally, one of the major causes of death is cardiovascular diseases (CVD), hence prompt and precise diagnosis is essential. This study’s primary objective is …

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A Machine Learning Approach to Nadi Pariksha: Detecting Dosha Imbalances

Raghav Mehra, Nandani Agarwal, Justina J

This research introduces PulseVision: AI-based Nadi Pariksha Health Diagnosis, an AI-based system for automated and upgraded traditional Ayurvedic diagnostic process of Nadi Pariksha. The analysis uses a panel-based dataset containing pulse-derived diagnostic attributes that have been preprocessed by the Random Forest classifier …

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Towards Sustainable Cancer Diagnosis: An Efficient Texture-Attention Framework with Enzyme-Inspired Optimization

Kanishk Agrawal, Javed Wasim, Raghav Mehra

The growing demand for sustainable and interpretable cancer diagnosis emphasizes the Artificial Intelligence (AI) models that balance accuracy with energy efficiency. Traditional methods face limitations such as high computational cost, poor generalizability, and class imbalance. To overcome these challenges, this work proposes …

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The Moderator AI: Classifying Harmful Language in Real-Time

Raghav Mehra, Kartik Rupal, Rishi Raj

The proliferation of toxic content on online platforms requires the development of robust, scalable, and automated solutions for content moderation. This paper presents a comprehensive, full-stack web application designed for real-time hate speech detection and classification. The system leverages a long-short-term memory …

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Evaluating Robustness of Neural Text Detectors in Generative AI Detection

Parasdeep, Mukesh Kumar Verma, Raghav Mehra, Kangan Soni et autres

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An Energy-Aware Hybrid Adaptive Preprocessing Framework for Attention-Guided Histopathological Cancer Detection

Kanishk Agrawal, Javed Wasim, Raghav Mehra

The demand for histopathological cancer diagnosis to be reproducible and high-quality spurs the need for preprocessing frameworks that increase image interpretability and optimize computational cost. However, current algorithmic approaches are mainly limited by noise sensitivity, staining variability, and energy load. To that …

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